We use the linear centered kernel alignment (linear \(\mathbb {CKA}\) ) [1]}
to measure the representation similarity between the models of different stages on CIFAR-10 (i.e. how much the model is updated/adjusted across stages), and the results are shown in Table REF  and Table REF .
In Table REF , the grey cells indicate that the given dataset \(\mathcal {D}_{\dot{t}}\)  is seen (trained) at both stages on comparison (i.e. \(\mathcal {D}_{\dot{t}}\)  has been used to train \(\mathcal {F}_t\)  and \(\mathcal {F}_{t+1}\) , where \(\dot{t} \le t\) ), and the white cells indicate that the given dataset \(\mathcal {D}_{\dot{t}}\)  is unseen at either one or both of the stages on comparison.
The column “Seen” in the Table REF  is the average linear \(\mathbb {CKA}\)  over the seen classes (i.e. related to grey cells in Table REF ) and the column “Unseen” is the unseen classes (i.e. related to white cells in Table REF ).
Higher linear \(\mathbb {CKA}\)  (what is achieved by our SDAF) means that the model changes less while learning new classes (thanks to the more generalised representation); hence, it has less forgetting.
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